Pre-clinical cognitive phenotypes for Alzheimer disease: a latent profile approach

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset

INTRODUCTION

Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods.

METHODS

Cluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression.

RESULTS

Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI).

DISCUSSION

Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.

Place of care in the last three years of life for Medicare beneficiaries

BACKGROUND

Most older adults prefer aging in place; however, patients with advanced illness often need institutional care. Understanding place of care trajectory patterns may inform patient-centered care planning and health policy decisions. The purpose of this study was to characterize place of care trajectories during the last three years of life.

METHODS

Linked administrative, claims, and assessment data were analyzed for a 10% random sample cohort of US Medicare beneficiaries who died in 2018, aged fifty or older, and continuously enrolled in Medicare during their last five years of life. A group-based trajectory modeling approach was used to classify beneficiaries based on the proportion of days of institutional care (hospital inpatient or skilled nursing facility) and skilled home care (home health care and home hospice) used in each quarter of the last three years of life. Associations between group membership and sociodemographic and clinical predictors were evaluated.

RESULTS

The analytic cohort included 199,828 Medicare beneficiaries. Nine place of care trajectory groups were identified, which were categorized into three clusters: home, skilled home care, and institutional care. Over half (59%) of the beneficiaries were in the home cluster, spending their last three years mostly at home, with skilled home care and institutional care use concentrated in the final quarter of life. One-quarter (27%) of beneficiaries were in the skilled home care cluster, with heavy use of skilled home health care and home hospice; the remaining 14% were in the institutional cluster, with heavy use of nursing home and inpatient care. Factors associated with both the skilled home care and institutional care clusters were female sex, Black race, a diagnosis of dementia, and Medicaid insurance. Extended use of skilled home care was more prevalent in southern states, and extended institutional care was more prevalent in midwestern states.

CONCLUSIONS

This study identified distinct patterns of place of care trajectories that varied in the timing and duration of institutional and skilled home care use during the last three years of life. Clinical, socioregional, and health policy factors influenced where patients received care. Our findings can help to inform personal and societal care planning.

The Parietal Lobe in Alzheimer’s Disease and Blindness

The progressive aging of the population will notably increase the burden of those diseases which leads to a disabling situation, such as Alzheimer’s disease (AD) and ophthalmological diseases that cause a visual impairment (VI). Eye diseases that cause a VI raise neuroplastic processes in the parietal lobe. Meanwhile, the aforementioned lobe suffers a severe decline throughout AD. From this perspective, diving deeper into the particularities of the parietal lobe is of paramount importance. In this article, we discuss the functions of the parietal lobe, review the parietal anatomical and pathophysiological peculiarities in AD, and also describe some of the changes in the parietal region that occur after VI. Although the alterations in the hippocampus and the temporal lobe have been well documented in AD, the alterations of the parietal lobe have been less thoroughly explored. Recent neuroimaging studies have revealed that some metabolic and perfusion impairments along with a reduction of the white and grey matter could take place in the parietal lobe during AD. Conversely, it has been speculated that blinding ocular diseases induce a remodeling of the parietal region which is observable through the improvement of the integration of multimodal stimuli and in the increase of the volume of this cortical region. Based on current findings concerning the parietal lobe in both pathologies, we hypothesize that the increased activity of the parietal lobe in people with VI may diminish the neurodegeneration of this brain region in those who are visually impaired by oculardiseases.

Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research

OBJECTIVE

Alzheimer's disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline.

METHODS

An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart).

RESULTS

EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual-spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach.

CONCLUSION

The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.

The Cognitive Connectome in Healthy Aging

Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.

Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.

Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.

Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.

Cognitive Phenotypes of Older Adults with Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment: The Czech Brain Aging Study

OBJECTIVE

To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning.

METHOD

Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error.

RESULTS

Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose-response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified).

CONCLUSIONS

Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.

The Uniform Dataset 3.0 Neuropsychological Battery: Factor Structure, Invariance Testing, and Demographically Adjusted Factor Score Calculation

OBJECTIVE

The goals of this study were to (1) specify the factor structure of the Uniform Dataset 3.0 neuropsychological battery (UDS3NB) in cognitively unimpaired older adults, (2) establish measurement invariance for this model, and (3) create a normative calculator for factor scores.

METHODS

Data from 2520 cognitively intact older adults were submitted to confirmatory factor analyses and invariance testing across sex, age, and education. Additionally, a subsample of this dataset was used to examine invariance over time using 1-year follow-up data (n = 1061). With the establishment of metric invariance of the UDS3NB measures, factor scores could be extracted uniformly for the entire normative sample. Finally, a calculator was created for deriving demographically adjusted factor scores.

RESULTS

A higher order model of cognition yielded the best fit to the data χ2(47) = 385.18, p < .001, comparative fit index = .962, Tucker-Lewis Index = .947, root mean square error of approximation = .054, and standardized root mean residual = .036. This model included a higher order general cognitive abilities factor, as well as lower order processing speed/executive, visual, attention, language, and memory factors. Age, sex, and education were significantly associated with factor score performance, evidencing a need for demographic correction when interpreting factor scores. A user-friendly Excel calculator was created to accomplish this goal and is available in the online supplementary materials.

CONCLUSIONS

The UDS3NB is best characterized by a higher order factor structure. Factor scores demonstrate at least metric invariance across time and demographic groups. Methods for calculating these factors scores are provided.

Memory profiles predict dementia over 23-28 years in normal but not successful aging

OBJECTIVES

Prospective studies suggest that memory deficits are detectable decades before clinical symptoms of dementia emerge. However, individual differences in long-term memory trajectories prior to diagnosis need to be further elucidated. The aim of the current study was to investigate long-term dementia and mortality risk for individuals with different memory trajectory profiles in a well-characterized population-based sample.

METHODS

1062 adults (aged 45-80 years) who were non-demented at baseline were followed over 23-28 years. Dementia and mortality risk were studied for three previously classified episodic memory trajectory groups: maintained high performance (Maintainers; 26%), average decline (Averages; 64%), and accelerated decline (Decliners; 12%), using multistate modeling to characterize individuals' transitions from an initial non-demented state, possibly to a state of dementia and/or death.

RESULTS

The memory groups showed considerable intergroup variability in memory profiles, starting 10-15 years prior to dementia diagnosis, and prior to death. A strong relationship between memory trajectory group and dementia risk was found. Specifically, Decliners had more than a fourfold risk of developing dementia compared to Averages. In contrast, Maintainers had a 2.6 times decreased dementia risk compared to Averages, and in addition showed no detectable memory decline prior to dementia diagnosis. A similar pattern of association was found for the memory groups and mortality risk, although only among non-demented.

CONCLUSION

There was a strong relationship between accelerated memory decline and dementia, further supporting the prognostic value of memory decline. The intergroup differences, however, suggest that mechanisms involved in successful memory aging may delay symptom onset.

Statistically Derived Subtypes and Associations with Cerebrospinal Fluid and Genetic Biomarkers in Mild Cognitive Impairment: A Latent Profile Analysis

OBJECTIVES

Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. Therefore, we examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically derived classes.

METHODS

A total of 806 participants diagnosed by means of the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on "robust" normal control performance (n=260). Calculated Z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes.

RESULTS

Statistical fit indices suggested a 3-class model was the optimal LPA solution. The three-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be apolipoprotein e4+ and have worse Alzheimer's disease CSF biomarkers than LPA-derived normal subjects.

CONCLUSIONS

Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent "false-positive" diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive. (JINS, 2017, 23, 564-576).

Age at surgery as a predictor of cognitive improvements in patients with drug-resistant temporal epilepsy

Temporal lobe epilepsy (TLE) surgery is an effective procedure that can produce cognitive changes. However, the prognostic factors related with cognitive outcomes need to be better understood. The aim of the present study is to know if age at surgery is a reliable predictor of verbal memory competence and considering factors such as: hemisphere; type of surgery; pre-surgical seizure frequency; and epilepsy duration. Sixty-one typically dominant patients with drug-resistant TLE (34 with left TLE [L-TLE] and 27 with right TLE [R-TLE]) underwent a neuropsychological assessment before and a year after surgery. Results showed that R-TLE patients had better evolution in short- and long-term verbal memory and naming than L-TLE patients (for all, p >.04). L-TLE patients also more frequently showed a strong and reliable decline in these functions than R-TLE patients. No effects for gender or type of surgery were found. From a multivariate approach, patients with improvements in verbal competence underwent surgery at earlier ages and suffered epilepsy for less time (for all, p <0.4). The relevance of age at surgery was confirmed as a predictor of long-term verbal memory changes, although the frequency of partial seizures also explains, at least partially, these changes. In addition, the frequency of partial seizures explains short-term verbal memory changes. These results emphasize the importance of early intervention, independently of the resected hemisphere, in order to minimize the cognitive side-effects of epilepsy treatment, as well the need to consider cognitive functions as related processes and network dependent.

Prefrontal-Parietal White Matter Volumes in Healthy Elderlies Are Decreased in Proportion to the Degree of Cardiovascular Risk and Related to Inhibitory Control Deficits

Cardiovascular risk (CVR) factors may be associated with poor cognitive functioning in elderlies and impairments in brain structure. Using MRI and voxel-based morphometry (VBM), we assessed regional white matter (WM) volumes in a population-based sample of individuals aged 65–75 years (n = 156), subdivided in three CVR subgroups using the Framingham Risk Score. Cognition was assessed using the Short Cognitive Performance Test. In high-risk subjects, we detected significantly reduced WM volume in the right juxtacortical dorsolateral prefrontal region compared to both low and intermediate CVR subgroups. Findings remained significant after accounting for the presence of the APOEε4 allele. Inhibitory control performance was negatively related to right prefrontal WM volume, proportionally to the degree of CVR. Significantly reduced deep parietal WM was also detected bilaterally in the high CVR subgroup. This is the first large study documenting the topography of CVR-related WM brain volume deficits. The significant association regarding poor response inhibition indicates that prefrontal WM deficits related to CVR are clinically meaningful, since inhibitory control is known to rely on prefrontal integrity.

Neuropsychological Subgroups in Non-Demented Parkinson's Disease: A Latent Class Analysis

BACKGROUND

Methods to detect early cognitive decline and account for heterogeneity of deficits in Parkinson's disease (PD) are needed. Quantitative methods such as latent class analysis (LCA) offer an objective approach to delineate discrete phenotypes of impairment.

OBJECTIVE

To identify discrete neurocognitive phenotypes in PD patients without dementia.

METHODS

LCA was applied to a battery of 8 neuropsychological measures to identify cognitive subtypes in a cohort of 199 non-demented PD patients. Two measures were analyzed from each of four domains: executive functioning, memory, visuospatial abilities, and language. Additional analyses compared groups on clinical characteristics and cognitive diagnosis.

RESULTS

LCA identified 3 distinct groups of PD patients: an intact cognition group (54.8%), an amnestic group (32.2%), and a mixed impairment group with dysexecutive, visuospatial and lexical retrieval deficits (13.1%). The two impairment groups had significantly lower instrumental activities of daily living ratings and greater motor symptoms than the intact group. Of those diagnosed as cognitively normal according to MDS criteria, LCA classified 23.2% patients as amnestic and 9.9% as mixed cognitive impairment.

CONCLUSIONS

Non-demented PD patients exhibit distinct neuropsychological profiles. One-third of patients with LCA-determined impairment were diagnosed as cognitively intact by expert consensus, indicating that classification using a statistical algorithm may improve detection of initial and subtle cognitive decline. This study also demonstrates that memory impairment is common in non-demented PD even when cognitive impairment is not clinically apparent. This study has implications for predicting eventual emergence of significant cognitive decline, and treatment trials for cognitive dysfunction in PD.

Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction

Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods

Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results

Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion

LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.

CANTAB object recognition and language tests to detect aging cognitive decline: an exploratory comparative study

Objective

The recognition of the limits between normal and pathological aging is essential to start preventive actions. The aim of this paper is to compare the Cambridge Neuropsychological Test Automated Battery (CANTAB) and language tests to distinguish subtle differences in cognitive performances in two different age groups, namely young adults and elderly cognitively normal subjects.

Method

We selected 29 young adults (29.9±1.06 years) and 31 older adults (74.1±1.15 years) matched by educational level (years of schooling). All subjects underwent a general assessment and a battery of neuropsychological tests, including the Mini Mental State Examination, visuospatial learning, and memory tasks from CANTAB and language tests. Cluster and discriminant analysis were applied to all neuropsychological test results to distinguish possible subgroups inside each age group.

Results

Significant differences in the performance of aged and young adults were detected in both language and visuospatial memory tests. Intragroup cluster and discriminant analysis revealed that CANTAB, as compared to language tests, was able to detect subtle but significant differences between the subjects.

Conclusion

Based on these findings, we concluded that, as compared to language tests, large-scale application of automated visuospatial tests to assess learning and memory might increase our ability to discern the limits between normal and pathological aging.

An empirically derived composite cognitive test score with improved power to track and evaluate treatments for preclinical Alzheimer's disease

BACKGROUND

There is growing interest in the evaluation of preclinical Alzheimer's disease (AD) treatments. As a result, there is a need to identify a cognitive composite that is sensitive to track preclinical AD decline to be used as a primary endpoint in treatment trials.

METHODS

Longitudinal data from initially cognitively normal, 70- to 85-year-old participants in three cohort studies of aging and dementia from the Rush Alzheimer's Disease Center were examined to empirically define a composite cognitive endpoint that is sensitive to detect and track cognitive decline before the onset of cognitive impairment. The mean-to-standard deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of cognitive tests/subtests drawn from the neuropsychological battery in cognitively normal participants who subsequently progressed to clinical stages of AD during 2- and 5-year periods, using data from those who remained unimpaired during the same period to correct for aging and practice effects. Combinations that performed well were then evaluated for representation of relevant cognitive domains, robustness across individual years before diagnosis, and occurrence of selected items within top performing combinations.

RESULTS

The optimal composite cognitive test score comprised seven cognitive tests/subtests with an MSDR = 0.964. By comparison, the most sensitive individual test score was Logical Memory Delayed Recall with an MSDR = 0.64.

CONCLUSIONS

We have identified a composite cognitive test score representing multiple cognitive domains that has improved power compared with the most sensitive single test item to track preclinical AD decline and evaluate preclinical AD treatments. We are confirming the power of the composite in independent cohorts and with other analytical approaches, which may result in refinements, have designated it as the primary endpoint in the Alzheimer's Prevention Initiative's preclinical treatment trials for individuals at high imminent risk for developing symptoms due to late-onset AD.

The Alzheimer's prevention initiative composite cognitive test score: sample size estimates for the evaluation of preclinical Alzheimer's disease treatments in presenilin 1 E280A mutation carriers

OBJECTIVE

To identify a cognitive composite that is sensitive to tracking preclinical Alzheimer's disease decline to be used as a primary end point in treatment trials.

METHOD

We capitalized on longitudinal data collected from 1995 to 2010 from cognitively unimpaired presenilin 1 (PSEN1) E280A mutation carriers from the world's largest known early-onset autosomal dominant Alzheimer's disease kindred to identify a composite cognitive test with the greatest statistical power to track preclinical Alzheimer's disease decline and estimate the number of carriers age 30 years and older needed to detect a treatment effect in the Alzheimer's Prevention Initiative's (API) preclinical Alzheimer's disease treatment trial. The mean-to-standard-deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of 1 to 7 cognitive tests/subtests drawn from the neuropsychological test battery in cognitively unimpaired mutation carriers during a 2- and 5-year follow-up period (n = 78 and 57), using data from noncarriers (n = 31 and 56) during the same time period to correct for aging and practice effects. Combinations that performed well were then evaluated for robustness across follow-up years, occurrence of selected items within top-performing combinations, and representation of relevant cognitive domains.

RESULTS

The optimal test combination included Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word List Recall, CERAD Boston Naming Test (high frequency items), Mini-Mental State Examination (MMSE) Orientation to Time, CERAD Constructional Praxis, and Raven's Progressive Matrices (Set A), with an MSDR of 1.62. This composite is more sensitive than using either the CERAD Word List Recall (MSDR = 0.38) or the entire CERAD-Col battery (MSDR = 0.76). A sample size of 75 cognitively normal PSEN1 E280A mutation carriers aged 30 years and older per treatment arm allows for a detectable treatment effect of 29% in a 60-month trial (80% power, P = .05).

CONCLUSIONS

We have identified a composite cognitive test score representing multiple cognitive domains that, compared to the most sensitive single test item, has improved power to track preclinical Alzheimer's disease decline in autosomal dominant Alzheimer's disease mutation carriers and to evaluate preclinical Alzheimer's disease treatments. This API composite cognitive test score will be used as the primary end point in the first API trial in cognitively unimpaired autosomal dominant Alzheimer's disease carriers within 15 years of their estimated age at clinical onset. We have independently confirmed our findings in a separate cohort of cognitively healthy older adults who progressed to the clinical stages of late-onset Alzheimer's disease, described in a separate report, and continue to refine the composite in independent cohorts and compared with other analytic approaches.